Related Experiment Video
Updated: May 8, 2026

Determining Immune System Suppression versus CNS Protection for Pharmacological Interventions in Autoimmune Demyelination
Published on: September 12, 2016
Distinguishing Chronic Inflammatory Demyelinating Polyneuropathy From Mimic Disorders: The Role of Statistical
Grace Swart1, Michael P Skolka1, Shahar Shelly1,2
1Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA.
Background And Aims:
Chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is difficult to distinguish from mimicking disorders, with misdiagnosis resulting in IVIG overutilization. We evaluate a clinical-electrophysiological model to facilitate CIDP versus mimic neuropathy prediction.
Methods:
Using the European Academy of Neurology/Peripheral Nerve Society (EAN/PNS) 2021 CIDP guidelines we derived 26 clinical and 144 nerve conduction variables. The model was generated and validated utilizing total CIDP (n = 129) and mimics (n = 309); including (1) IgG4-nodopathies; (2) POEMS (polyneuropathy-organomegaly-endocrinopathy-monoclonal protein-skin changes); (3) anti-myelin-associated-glycoprotein; (4) paraneoplastic; (5) Waldenström B-cell lymphoma; (6) diabetic neuropathies; (7) amyloidosis; (8) Charcot-Marie-Tooth; (9) motor neuropathies/neuronopathies; and (10) idiopathic-inflammatory-myopathies.
Results:
We analyzed 9282 clinical and 51 408 electrophysiological data points. Univariate analysis identified 11 of 26 clinical variables with significant odds ratios. A multivariate regression model using four clinical and two electrophysiologic variables achieved 93% area-under-curve (95% CI 91-95): progression over 8 weeks (OR 40.66, 95% CI 5.31-311.36), absent autonomic involvement (OR 17.82, 95% CI 2.93-108.24), absent muscle atrophy (OR 16.65, 95% CI 3.27-84.73), proximal weakness (OR 3.63, 95% CI 1.58-8.33), ulnar motor conduction velocity slowing < 35.7 m/s (OR 5.21, 95% CI 2.13-12.76), and ulnar motor conduction block (OR 13.37, 95% CI 2.47-72.40). A web-based probability calculator (https://news.mayocliniclabs.com/cidp-calculator/) was developed, with 100% sensitivity and 68% specificity at a 92% probability threshold. Specificity improved to 93% when considering "red flags," electrophysiologic criteria, and laboratory testing.
Interpretation:
A probability calculator using clinical electrophysiological variables assists CIDP differentiation from mimics, with scores below 92% unlikely to have CIDP. The highest specificity is achieved by considering clinical "red flags," electrophysiologic demyelination, and laboratory testing.
Related Concept Videos
Mechanistic Models: Compartment Models in Individual and Population Analysis
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Statistical Methods for Analyzing Epidemiological Data

